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Found 12 papers, 4 papers with code
FourierNets enable the design of highly non-local optical encoders for computational imaging
More challenging computational imaging applications, such as 3D snapshot microscopy which compresses 3D volumes into single 2D images, require a highly non-local optical encoder.
Learning Guided Electron Microscopy with Active Acquisition
Single-beam scanning electron microscopes (SEM) are widely used to acquire massive data sets for biomedical study, material analysis, and fabrication inspection.
Teaching deep neural networks to localize single molecules for super-resolution microscopy
We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art.
A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System
What can we learn from a connectome?
Discrete flow posteriors for variational inference in discrete dynamical systems
Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e. g. factorised) approximate posteriors in which sampling is an efficient computation that fully exploits GPU parallelism.
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit
Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo.
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedded across multiple measurements.
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Calcium imaging permits optical measurement of neural activity.
Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction
Our extension consists of two parts: First, we present a quasi-linear method to compute the loss gradient, improving over the original quadratic algorithm.
Ranked #1 on
Brain Image Segmentation
on FIB-25 Synaptic Sites
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals.
Learning to Agglomerate Superpixel Hierarchies
An agglomerative clustering algorithm merges the most similar pair of clusters at every iteration.
Maximin affinity learning of image segmentation
We present the first machine learning algorithm for training a classifier to produce affinity graphs that are good in the sense of producing segmentations that directly minimize the Rand index, a well known segmentation performance measure.
